Prof. Alexander G. Ororbia is a researcher in the field of bio-inspired artificial intelligence, working on on mortal computation and neurobiologically-plausible learning algorithms. Ororbia takes us on a tour of brain-inspired AI, discussing how concepts like predictive coding, forward-only learning, and neural generative coding can lead to more efficient and adaptable AI systems. He explores the how we might implement these bio-inspired approaches on neuromorphic hardware, and shares his vision for a future where AI systems are more closely aligned with biological intelligence. MLST is sponsored by Tufa Labs: Are you interested in working on ARC and cutting-edge AI research with the MindsAI team (current ARC winners)? Focus: ARC, LLMs, test-time-compute, active inference, system2 reasoning, and more. Future plans: Expanding to complex environments like Warcraft 2 and Starcraft 2. Interested? Apply for an ML research position: benjamin@tufa.ai Prof. Alexander Ororbia https://www.rit.edu/directory/agovcs-alexander-ororbia https://scholar.google.com/citations?user=IAFscrMAAAAJ&hl=en https://www.linkedin.com/in/alexander-ororbia-ii-8b6a29115/ TOC: 1. Foundations of Bio-Inspired AI [00:00:00] 1.1 Introduction to Bio-Inspired AI and Mortal Computation [00:04:50] 1.2 Principles of Mortal Computation and Biomimetic AI [00:17:41] 1.3 Markov Blankets and Free Energy Principle [00:24:38] 1.4 MILLS Framework and Biological Systems 2. Alternative Learning Paradigms [00:31:00] 2.1 Challenging Backpropagation: Overview of Alternatives [00:31:49] 2.2 Predictive Coding and Free Energy Principle [00:41:52] 2.3 Biologically Plausible Credit Assignment Methods [00:50:11] 2.4 Taxonomy of Bio-inspired Learning Algorithms 3. Advanced Bio-Inspired AI Implementations [00:59:30] 3.1 Forward-Only Learning and NGC Learn Implementation [01:03:25] 3.2 Stability-Plasticity Dilemma and Bio-Inspired Solutions [01:09:00] 3.3 Neuromorphic Hardware Landscape and Challenges [01:12:58] 3.4 Neural Generative Coding and Predictive Coding Advancements [01:20:36] 3.5 Latent Space Predictions in Forward-Only Learning REFS: The Levin Lab https://drmichaellevin.org/ Mortal Computation: A Foundation for Biomimetic Intelligence https://arxiv.org/pdf/2311.09589 The Forward-Forward Algorithm: Some Preliminary Investigations https://arxiv.org/pdf/2212.13345 Good regulator https://en.wikipedia.org/wiki/Good_regulator The free-energy principle: a rough guide to the brain? https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20principle%20-%20a%20rough%20guide%20to%20the%20brain.pdf Hebbian theory https://en.wikipedia.org/wiki/Hebbian_theory There’s Plenty of Room Right Here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046700/ Active Inference: The Free Energy Principle in Mind, Brain, and Behavior https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment https://arxiv.org/pdf/2312.09257 Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects https://www.nature.com/articles/nn0199_79 Hopfield network https://en.wikipedia.org/wiki/Hopfield_network A Tutorial on Energy-Based Learning https://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf A Learning Algorithm for Boltzmann Machines https://www.cs.toronto.edu/~hinton/absps/cogscibm.pdf A Review of Neuroscience-Inspired Machine Learning https://arxiv.org/pdf/2403.18929 Spiking neural predictive coding for continually learning from data streams https://www.sciencedirect.com/science/article/pii/S0925231223004150 Neuroanatomy, Basal Ganglia https://www.ncbi.nlm.nih.gov/books/NBK537141/ Intel Loihi 2 https://www.intel.com/content/www/us/en/research/neuromorphic-computing-loihi-2-technology-brief.html IBM TrueNorth https://research.ibm.com/publications/truenorth-design-and-tool-flow-of-a-65-mw-1-million-neuron-programmable-neurosynaptic-chip GC (Generative Coding) https://www.researchgate.net/publication/360036687_The_neural_coding_framework_for_learning_generative_models NeuroEvolution of Augmenting Topologies (NEAT) https://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods https://arxiv.org/pdf/2205.11508 A Path Towards Autonomous Machine Intelligence (Yann LeCun) https://openreview.net/pdf?id=BZ5a1r-kVsf Test-Time Model Adaptation with Only Forward Passes https://arxiv.org/pdf/2404.01650v2 Show notes/transcript: https://www.dropbox.com/scl/fi/lbnktvlelaczh9a7qsddq/Alex.pdf?rlkey=931uhedkoapqgzfe0lnmw6tgr&st=sr3abzzr&dl=0